Snowflake vs. MongoDB: Data Management Comparison

Snowflake and MongoDB Atlas are database and data management platforms that are well-regarded in the industry and generally score well in analyst evaluations. They are in heavy demand from organizations seeking to store, manage, and glean data analytics insight from the vast troves of data at their disposal.
But which of these well-respected data analytics platforms is the right choice? Both Snowflake and MongoDB Atlas provide the volume, speed, and quality demanded by the analytics applications they typically support. Yet there are as many similarities as there are differences.
Therefore, the choice between Snowflake and MongoDB Atlas often boils down to platform preference and suitability for an organization’s data strategy. Let’s review them against several criteria and evaluate which is best in each area and which is best overall.
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Snowflake vs. MongoDB: Key Features
Snowflake
Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data. Offered as a software-as-a-service (SaaS) model, Snowflake offers the following features:

An SQL database engine manages how information is stored and processes queries against virtual warehouses within the overall warehouse, each in its own cluster node independent of others.
Cloud services provide authentication, infrastructure management, queries, access controls, and so on.
The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.

MongoDB Atlas
MongoDB Atlas is an open-source, cloud-hosted document database that is scalable and includes querying and indexing. MongoDB’s key database features include:

The document data model supports JSON, and its query language is said to be simple for developers to learn and use.
Functionality includes automatic failover, horizontal scaling, and the ability to assign data to a location.
An Atlas Data Lake capability is in preview that can automatically extract and optimize data into a fully managed data lake store to isolate analytical workloads and perform large-scale analytics.
MongoDB Atlas takes advantage of cloud object storage.
Because it enhances the data on ingestion and rebalances it as needed, MongoDB Atlas is said to deliver good performance and scalability.

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Which Is Best for Database and Data Management Features?
MongoDB has assembled a large catalog of services and functions over the past 10 years. Those familiar with open source and those with developer skills will appreciate it. 
For everyone else, Snowflake is probably the best choice. Gartner Peer Reviews gave both a good score, but Snowflake came out ahead.
Snowflake vs. MongoDB: Support and Ease of Use
Snowflake
The Snowflake platform is said to be user-friendly, offering an intuitive SQL interface that makes it easy to get set up and running. Other support and database features that help it stand out include:

Snowflake provides 24/7 live support.
It automates data vacuuming, compression, diagnosis, and other features.
There is also no need to copy data during scale up operations with Snowflake.
Snowflake supports structured and semi-structured data.

Some users, though, state that a lack of flexibility in areas such as resizing can lead to extra expense and long hours of maintenance. Therefore, organizations with large or growing databases may not find Snowflake suitable for their data management needs.
MongoDB Atlas
MongoDB Atlas is considered user friendly and intuitive by those familiar with the platform and with open-source tools in general. As a cloud database service:

It has excellent data distribution and mobility capabilities that are prized by developers.
It has good support for Amazon Web Services (AWS), Azure, and Google Cloud.
MongoDB supports structured, semi-structured, and unstructured data.

That said, some users complained that it lacks certain capabilities such as stored procedures, functions, and triggers. This inhibits its ability to write enterprise-class business logic at the database level or to deal with complex transactions.
Also see: Data Analytics Trends 
Which Is Best for Support and Ease of Use?
Snowflake wins for general use cases, but MongoDB is typically the best choice for developers.
Snowflake vs. MongoDB: Security
Snowflake
Snowflake boasts always-on encryption, along with network isolation, and other robust security features. Its security features come in tiers and each higher tier costs more. But on the plus side, you don’t end up paying for security features you don’t need or want.
MongoDB Atlas
With MongoDB, the better the version the better the security. The free and shared versions have basic security features but lack some of the advanced features of the dedicated and enterprise versions. The dedicated MongoDB version, for example, is said to offer consistent performance, unlimited scaling, built-in defaults for access, end-to-end encryption, and always on authentication.
Which Is Best for Security?
Snowflake wins on security.
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Snowflake vs. MongoDB: Integration
Snowflake
Snowflake, too, is on the AWS Marketplace but is not as tightly integrated there as MongoDB. In some cases, users comment that it can be challenging to integrate Snowflake with other tools. But in other cases, Snowflake integrates well with applications such as Tableau, Apache Spark, IBM Cognos, and Qlik. Those using these tools will find analysis easy to accomplish.
MongoDB Atlas
The MongoDB Atlas developer data platform is well-integrated within the cloud. This multicloud database and data management platform can run anywhere in the world. As such, it can be deployed in over 90 regions on AWS, Azure, and Google Cloud.
Those using it can expand to be global, multi-region, or multicloud when needed while providing low latency. A unified query application programming interface (API) is available to access and query data. Data stored in Atlas is instantly available to use with the rest of the platform, from full-text search to analytics to visualizations.
It includes drivers for 10+ languages, and many other open-source drivers are available for the platform to integrate with dozens more systems and platforms.
Which Is Best for Integration?
Overall, MongoDB wins due to its more comprehensive integration with the hyperscalers.
Snowflake vs. MongoDB: Pricing
Snowflake
Snowflake keeps compute and storage separate in its pricing structure. It provides concurrency scaling automatically with all editions at no extra cost. Snowflake pricing, however, is a little complex with several editions from basic on up, and prices rise as you move up the tiers. Roughly speaking, Snowflake costs about $40 a month.
MongoDB Atlas
Several versions on MongoDB Atlas are also available. A shared free version, for example, provides a modest amount of storage and shared RAM.
The dedicated version comes with far more performance, advanced security features, and unlimited scaling. Priced at $57 a month or an estimated 8 cents an hour, the dedicated version is aimed at production applications with sophisticated workload requirements and comes with advanced configuration tools.
Enterprise versions of the database are also available with further advanced features and the highest level of performance. Pricing for this version was unavailable. However, it comes with the MongoDB Enterprise Server, the Ops Manager management platform for MongoDB, Kubernetes integration, another batch of security features, business intelligence integration and visualization, and on-demand training.
Also see: Real Time Data Management Trends
Which Is Best for Pricing?
The differences between MongoDB and Snowflake in terms of offerings and platforms make it difficult to do a true apples-to-apples comparison on pricing. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements.
This is a close one, as it varies from use case to use case. Some may find MongoDB’s free, shared, and community versions perfectly adequate for their needs. Those with advanced developer or enterprise needs may be happy to pay more to have the top-of-the-line MongoDB capabilities.
That said, Snowflake may work better and be cheaper in many use cases. Yet, pricing can escalate on Snowflake when higher tiers become involved.
Choosing Between Snowflake and MongoDB
Snowflake and MongoDB Atlas are excellent database and data warehouses for analysis purposes. Each has its pros and cons. It all comes down to usage patterns, data volumes, workloads, and data strategies.
MongoDB is a no-brainer for developers; after all, it was built originally as a database specifically for the developer community. Since its inception, tools and capabilities have been added based on the needs of its large developer and open-source ecosystem. It is the clear favorite of those in the open-source community but may not be basic enough for organizations lacking skilled internal staff.
In those cases, Snowflake’s ability to split compute and storage pricing makes it more malleable to different use cases that lie outside of the open-source and developer field. Developers, too, may prefer it depending on the platforms they utilize. Organizations working closely with Tableau, Apache Spark, IBM Cognos, and Qlik, for example, may prefer Snowflake due to its focus on those tools and platforms.
But these are generalities and won’t always pan out. Each business needs to research how costs will work out for them. It is up to them to determine via good research – from platform toolset to their own data workload – which of these two platforms will suit their data needs best.
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